8.5 Volatility Forecasting in South Africa 🇿🇦

In this assignment you'll build a model to predict stock volatility for the telecommunications company MTN Group.

Working with APIs¶

Task 8.5.1: Create a URL to get all the stock data for MTN Group ("MTNOY") from AlphaVantage in JSON format. Be sure to use the https://learn-api.wqu.edu hostname. And don't worry: your submission won't include your API key!

Task 8.5.2: Create an HTTP request for the URL you created in the previous task. The grader will evaluate your work by looking at the ticker symbol in the "Meta Data" key-value pair in your response.

Task 8.5.3: Get status code of your response and assign it to the variable response_code.

Test-Driven Development¶

Task 8.5.4: Create a DataFrame df_mtnoy with all the stock data for MTN. Make sure that the DataFrame has the correct type of index and column names. The grader will evaluate your work by looking at the row in df_mtnoy for 6 December 2021.

Task 8.5.5: Connect to the database whose name is stored in the .env file for this project. Be sure to set the check_same_thread argument to False. Assign the connection to the variable connection. The grader will evaluate your work by looking at the database location assigned to connection.

Task 8.5.6: Insert df_mtnoy into your database. The grader will evaluate your work by looking at the first five rows of the MTNOY table in the database.

Task 8.5.7: Read the MTNOY table from your database and assign the output to df_mtnoy_read. The grader will evaluate your work by looking at the row for 27 April 2022.

Predicting Volatility¶

Prepare Data¶

Task 8.5.8: Create a Series y_mtnoy with the 2,500 most recent returns for MTN. The grader will evaluate your work by looking at the volatility for 9 August 2022.

Task 8.5.9: Calculate daily volatility for y_mtnoy, and assign the result to mtnoy_daily_volatility.

Task 8.5.10: Calculate the annual volatility for y_mtnoy, and assign the result to mtnoy_annual_volatility.

Task 8.5.11: Create a time series line plot for y_mtnoy. Be sure to label the x-axis "Date", the y-axis "Returns", and use the title "Time Series of MTNOY Returns".

Task 8.5.12: Create an ACF plot of the squared returns for MTN. Be sure to label the x-axis "Lag [days]", the y-axis "Correlation Coefficient", and use the title "ACF of MTNOY Squared Returns".

Task 8.5.13: Create a PACF plot of the squared returns for MTN. Be sure to label the x-axis "Lag [days]", the y-axis "Correlation Coefficient", and use the title "PACF of MTNOY Squared Returns".

Task 8.5.14: Create a training set y_mtnoy_train that contains the first 80% of the observations in y_mtnoy.

Build Model¶

Task 8.5.15: Build and fit a GARCH model using the data in y_mtnoy. Try different values for p and q, using the summary to assess its performance. The grader will evaluate whether your model is the correct data type.

Task 8.5.16: Plot the standardized residuals for your model. Be sure to label the x-axis "Date", the y-axis "Value", and use the title "MTNOY GARCH Model Standardized Residuals".

Task 8.5.17: Create an ACF plot of the squared, standardized residuals of your model. Be sure to label the x-axis "Lag [days]", the y-axis "Correlation Coefficient", and use the title "ACF of MTNOY GARCH Model Standardized Residuals".

Model Deployment¶

Ungraded Task: If it's not already running, start your app server. WQU WorldQuant University Applied Data Science Lab QQQQ

Task 8.5.18: Change the fit method of your GarchModel class so that, when a model is done training, two more attributes are added to the object: self.aic with the AIC for the model, and self.bic with the BIC for the model. When you're done, use the cell below to check your work.

Task 8.5.19: Change the fit_model function in the main module so that the "message" it returns includes the AIC and BIC scores. For example, the message should look something like this:

"Trained and saved 'models/2022-10-12T23:10:06.577238_MTNOY.pkl'. Metrics: AIC 9892.184665169907, BIC 9914.588275008075."

When you're done, use the cell below to check your work.

Task 8.5.20: Create a post request to hit the "/fit" path running at "http://localhost:8008". You should train a GARCH(1,1) model on 2500 observations of the MTN data you already downloaded. Pass in your parameters as a dictionary using the json argument. The grader will evaluate the JSON of your response.

Task 8.5.21: Create a post request to hit the "/predict" path running at "http://localhost:8008". You should get the 5-day volatility forecast for MTN. When you're satisfied, submit your work to the grader.

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8.4. Model Deployment

Ready for deployment! Over the last three lessons, we've built all the pieces we need for our application. We have a module for getting and storing our data. We have the code to train our model and clean its predictions. In this lesson, we're going to put all those pieces together and deploy our model with an API that others can use to train their own models and predict volatility. We'll start by creating a model for all the code we created in the last lesson. Then we'll complete our main module, which will hold our FastAPI application with two paths: one for model training and one for prediction. Let's jump in!

Model Module¶

We created a lot of code in the last lesson to building, training, and making predictions with our GARCH(1,1) model. We want this code to be reusable, so let's put it in its own module.

Let's start by instantiating a repository that we'll use for testing our module as we build.

Task 8.4.1: Create a SQLRepository named repo. Be sure that it's attached to a SQLite connection.

• Open a connection to a SQL database using sqlite3.

Now that we have the repo ready, we'll shift to our model module and build a GarchModel class to hold all our code from the last lesson.

Task 8.4.2: In the model module, create a definition for a GarchModel model class. For now, it should only have an __init__ method. Use the docstring as a guide. When you're done, test your class using the assert statements below.

• What's a class?
• Write a class definition in Python.
• Write a class method in Python.
• What's an assert statement?
• Write an assert statement in Python.

Task 8.4.3: Turn your wrangle_data function from the last lesson into a method for your GarchModel class. When you're done, use the assert statements below to test the method by getting and wrangling data for the department store Shoppers Stop.

• What's a function?
• Write a function in Python.
• Write a class method in Python.
• What's an assert statement?
• Write an assert statement in Python.

Task 8.4.4: Using your code from the previous lesson, create a fit method for your GarchModel class. When you're done, use the code below to test it.

• Write a class method in Python.
• What's an assert statement?
• Write an assert statement in Python.WQU WorldQuant University Applied Data Science Lab QQQQ

Task 8.4.5: Using your code from the previous lesson, create a predict_volatility method for your GarchModel class. Your method will need to return predictions as a dictionary, so you'll need to add your clean_prediction function as a helper method. When you're done, test your work using the assert statements below.

• Write a class method in Python.
• Write a function in Python.
• What's an assert statement?
• Write an assert statement in Python.

Things are looking good! There are two last methods that we need to add to our GarchModel so that we can save a trained model and then load it when we need it. When we learned about saving and loading files in Project 5, we used a context handler. This time, we'll streamline the process using the joblib library. We'll also start writing our filepaths more programmatically using the os library.

Task 8.4.6: Create a dump method for your GarchModel class. It should save the model assigned to the model attribute to the folder specified in your configuration settings. Use the docstring as a guide, and then test your work below.

• Write a class method in Python.
• Save an object using joblib.
• Create a file path using os.

Task 8.4.7: Create a load function below that will take a ticker symbol as input and return a model. When you're done, use the next cell to load the Shoppers Stop model you saved in the previous task.

• Handle errors using try and except blocks in Python.
• Create a file path using os.
• Raise an Exception in Python.

Task 8.4.8: Transform your load function into a method for your GarchModel class. When you're done, test the method using the assert statements below.

• Write a class method in Python.
• What's an assert statement?
• Write an assert statement in Python.